Overview

Dataset statistics

Number of variables20
Number of observations41106
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory160.0 B

Variable types

Categorical6
Numeric14

Alerts

track has a high cardinality: 35860 distinct valuesHigh cardinality
artist has a high cardinality: 11904 distinct valuesHigh cardinality
uri has a high cardinality: 40560 distinct valuesHigh cardinality
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
time_signature is highly imbalanced (72.9%)Imbalance
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 5130 (12.5%) zerosZeros
instrumentalness has 11709 (28.5%) zerosZeros

Reproduction

Analysis started2023-09-26 15:10:25.194414
Analysis finished2023-09-26 15:11:47.101503
Duration1 minute and 21.91 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct35860
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
Falling
 
17
Angel
 
13
Hold On
 
12
Crazy
 
12
You
 
12
Other values (35855)
41040 

Length

Max length254
Median length135
Mean length18.599645
Min length1

Characters and Unicode

Total characters764557
Distinct characters333
Distinct categories19 ?
Distinct scripts9 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32370 ?
Unique (%)78.7%

Sample

1st rowNachtlager N. Granada - The Lower Castle Yard
2nd rowHow's It Going To Be
3rd rowHeavenly Perverse
4th rowEra um Garoto Que Como Eu Amava os Beatles e os Rolling Stones (C'era un ragazzo)
5th rowClavel Sevillano

Common Values

ValueCountFrequency (%)
Falling 17
 
< 0.1%
Angel 13
 
< 0.1%
Hold On 12
 
< 0.1%
Crazy 12
 
< 0.1%
You 12
 
< 0.1%
Stay 11
 
< 0.1%
Forever 11
 
< 0.1%
Runaway 11
 
< 0.1%
Happy 11
 
< 0.1%
I Love You 10
 
< 0.1%
Other values (35850) 40986
99.7%

Length

2023-09-26T15:11:47.321581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 5263
 
3.6%
3790
 
2.6%
you 2722
 
1.9%
i 2364
 
1.6%
a 2154
 
1.5%
of 2054
 
1.4%
love 1947
 
1.3%
me 1825
 
1.2%
in 1773
 
1.2%
to 1602
 
1.1%
Other values (21633) 121403
82.6%

Most occurring characters

ValueCountFrequency (%)
105793
 
13.8%
e 70265
 
9.2%
o 48805
 
6.4%
a 45336
 
5.9%
n 38932
 
5.1%
i 36099
 
4.7%
r 33946
 
4.4%
t 33026
 
4.3%
l 24611
 
3.2%
s 24163
 
3.2%
Other values (323) 303581
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 493359
64.5%
Uppercase Letter 134711
 
17.6%
Space Separator 105793
 
13.8%
Other Punctuation 13799
 
1.8%
Decimal Number 6191
 
0.8%
Dash Punctuation 3808
 
0.5%
Open Punctuation 3033
 
0.4%
Close Punctuation 3030
 
0.4%
Other Letter 684
 
0.1%
Nonspacing Mark 48
 
< 0.1%
Other values (9) 101
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
י 67
 
9.8%
ל 45
 
6.6%
ה 39
 
5.7%
ו 32
 
4.7%
א 24
 
3.5%
ב 24
 
3.5%
ר 22
 
3.2%
ד 20
 
2.9%
18
 
2.6%
נ 16
 
2.3%
Other values (123) 377
55.1%
Lowercase Letter
ValueCountFrequency (%)
e 70265
14.2%
o 48805
9.9%
a 45336
 
9.2%
n 38932
 
7.9%
i 36099
 
7.3%
r 33946
 
6.9%
t 33026
 
6.7%
l 24611
 
5.0%
s 24163
 
4.9%
h 19124
 
3.9%
Other values (76) 119052
24.1%
Uppercase Letter
ValueCountFrequency (%)
T 12976
 
9.6%
S 10250
 
7.6%
M 9982
 
7.4%
I 8890
 
6.6%
A 8792
 
6.5%
L 8330
 
6.2%
B 7678
 
5.7%
D 6607
 
4.9%
C 6330
 
4.7%
W 6240
 
4.6%
Other values (36) 48636
36.1%
Other Punctuation
ValueCountFrequency (%)
' 5567
40.3%
. 2249
16.3%
, 2172
 
15.7%
: 1220
 
8.8%
" 783
 
5.7%
/ 671
 
4.9%
& 322
 
2.3%
! 307
 
2.2%
? 301
 
2.2%
* 53
 
0.4%
Other values (11) 154
 
1.1%
Nonspacing Mark
ValueCountFrequency (%)
11
22.9%
10
20.8%
7
14.6%
6
12.5%
4
 
8.3%
3
 
6.2%
2
 
4.2%
2
 
4.2%
̃ 1
 
2.1%
1
 
2.1%
Decimal Number
ValueCountFrequency (%)
1 1283
20.7%
2 1074
17.3%
0 1006
16.2%
9 720
11.6%
7 378
 
6.1%
4 377
 
6.1%
6 371
 
6.0%
3 344
 
5.6%
5 328
 
5.3%
8 310
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 3799
99.8%
4
 
0.1%
3
 
0.1%
2
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 13
48.1%
= 8
29.6%
~ 3
 
11.1%
> 3
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 2939
96.9%
[ 92
 
3.0%
2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 2938
97.0%
] 92
 
3.0%
Final Punctuation
ValueCountFrequency (%)
29
90.6%
3
 
9.4%
Modifier Symbol
ValueCountFrequency (%)
´ 11
68.8%
` 5
31.2%
Other Symbol
ValueCountFrequency (%)
° 3
75.0%
¦ 1
 
25.0%
Modifier Letter
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
105793
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 627988
82.1%
Common 135754
 
17.8%
Hebrew 408
 
0.1%
Thai 230
 
< 0.1%
Cyrillic 89
 
< 0.1%
Han 48
 
< 0.1%
Katakana 27
 
< 0.1%
Hiragana 12
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70265
 
11.2%
o 48805
 
7.8%
a 45336
 
7.2%
n 38932
 
6.2%
i 36099
 
5.7%
r 33946
 
5.4%
t 33026
 
5.3%
l 24611
 
3.9%
s 24163
 
3.8%
h 19124
 
3.0%
Other values (94) 253681
40.4%
Common
ValueCountFrequency (%)
105793
77.9%
' 5567
 
4.1%
- 3799
 
2.8%
( 2939
 
2.2%
) 2938
 
2.2%
. 2249
 
1.7%
, 2172
 
1.6%
1 1283
 
0.9%
: 1220
 
0.9%
2 1074
 
0.8%
Other values (46) 6720
 
5.0%
Han
ValueCountFrequency (%)
2
 
4.2%
2
 
4.2%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (36) 36
75.0%
Thai
ValueCountFrequency (%)
18
 
7.8%
16
 
7.0%
15
 
6.5%
13
 
5.7%
11
 
4.8%
10
 
4.3%
10
 
4.3%
10
 
4.3%
9
 
3.9%
8
 
3.5%
Other values (31) 110
47.8%
Cyrillic
ValueCountFrequency (%)
о 12
 
13.5%
а 10
 
11.2%
н 7
 
7.9%
с 6
 
6.7%
р 5
 
5.6%
т 4
 
4.5%
е 4
 
4.5%
м 4
 
4.5%
ь 4
 
4.5%
к 3
 
3.4%
Other values (20) 30
33.7%
Hebrew
ValueCountFrequency (%)
י 67
16.4%
ל 45
11.0%
ה 39
 
9.6%
ו 32
 
7.8%
א 24
 
5.9%
ב 24
 
5.9%
ר 22
 
5.4%
ד 20
 
4.9%
נ 16
 
3.9%
ת 15
 
3.7%
Other values (17) 104
25.5%
Katakana
ValueCountFrequency (%)
3
 
11.1%
3
 
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (9) 9
33.3%
Hiragana
ValueCountFrequency (%)
2
16.7%
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Inherited
ValueCountFrequency (%)
̃ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 761549
99.6%
None 2135
 
0.3%
Hebrew 408
 
0.1%
Thai 230
 
< 0.1%
Cyrillic 89
 
< 0.1%
Punctuation 55
 
< 0.1%
CJK 48
 
< 0.1%
Katakana 30
 
< 0.1%
Hiragana 12
 
< 0.1%
Diacriticals 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105793
 
13.9%
e 70265
 
9.2%
o 48805
 
6.4%
a 45336
 
6.0%
n 38932
 
5.1%
i 36099
 
4.7%
r 33946
 
4.5%
t 33026
 
4.3%
l 24611
 
3.2%
s 24163
 
3.2%
Other values (79) 300573
39.5%
None
ValueCountFrequency (%)
é 436
20.4%
ó 258
12.1%
ç 187
 
8.8%
ã 168
 
7.9%
á 158
 
7.4%
í 139
 
6.5%
ê 84
 
3.9%
è 83
 
3.9%
ñ 82
 
3.8%
ä 62
 
2.9%
Other values (51) 478
22.4%
Hebrew
ValueCountFrequency (%)
י 67
16.4%
ל 45
11.0%
ה 39
 
9.6%
ו 32
 
7.8%
א 24
 
5.9%
ב 24
 
5.9%
ר 22
 
5.4%
ד 20
 
4.9%
נ 16
 
3.9%
ת 15
 
3.7%
Other values (17) 104
25.5%
Punctuation
ValueCountFrequency (%)
29
52.7%
12
21.8%
4
 
7.3%
3
 
5.5%
3
 
5.5%
2
 
3.6%
1
 
1.8%
1
 
1.8%
Thai
ValueCountFrequency (%)
18
 
7.8%
16
 
7.0%
15
 
6.5%
13
 
5.7%
11
 
4.8%
10
 
4.3%
10
 
4.3%
10
 
4.3%
9
 
3.9%
8
 
3.5%
Other values (31) 110
47.8%
Cyrillic
ValueCountFrequency (%)
о 12
 
13.5%
а 10
 
11.2%
н 7
 
7.9%
с 6
 
6.7%
р 5
 
5.6%
т 4
 
4.5%
е 4
 
4.5%
м 4
 
4.5%
ь 4
 
4.5%
к 3
 
3.4%
Other values (20) 30
33.7%
Katakana
ValueCountFrequency (%)
3
 
10.0%
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (11) 11
36.7%
Hiragana
ValueCountFrequency (%)
2
16.7%
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
CJK
ValueCountFrequency (%)
2
 
4.2%
2
 
4.2%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (36) 36
75.0%
Diacriticals
ValueCountFrequency (%)
̃ 1
100.0%

artist
Categorical

Distinct11904
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
Traditional
 
207
Harry Belafonte
 
140
P. Susheela
 
130
Ennio Morricone
 
128
Jerry Goldsmith
 
124
Other values (11899)
40377 

Length

Max length88
Median length79
Mean length13.621783
Min length1

Characters and Unicode

Total characters559937
Distinct characters148
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6698 ?
Unique (%)16.3%

Sample

1st rowTraditional
2nd rowThird Eye Blind
3rd rowDimmu Borgir
4th rowOs Incríveis
5th rowJavier Solís

Common Values

ValueCountFrequency (%)
Traditional 207
 
0.5%
Harry Belafonte 140
 
0.3%
P. Susheela 130
 
0.3%
Ennio Morricone 128
 
0.3%
Jerry Goldsmith 124
 
0.3%
Vicente Fernández 116
 
0.3%
Antônio Carlos Jobim 109
 
0.3%
Gilberto Gil 88
 
0.2%
Frank Zappa 80
 
0.2%
Raimon 79
 
0.2%
Other values (11894) 39905
97.1%

Length

2023-09-26T15:11:47.675897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 4530
 
4.9%
1842
 
2.0%
featuring 1658
 
1.8%
john 586
 
0.6%
band 463
 
0.5%
of 419
 
0.5%
johnny 276
 
0.3%
orchestra 269
 
0.3%
joe 265
 
0.3%
brown 263
 
0.3%
Other values (11282) 82232
88.6%

Most occurring characters

ValueCountFrequency (%)
e 52071
 
9.3%
51697
 
9.2%
a 43216
 
7.7%
n 35841
 
6.4%
i 33715
 
6.0%
r 33334
 
6.0%
o 32189
 
5.7%
l 23372
 
4.2%
s 22072
 
3.9%
t 21981
 
3.9%
Other values (138) 210449
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 406151
72.5%
Uppercase Letter 95272
 
17.0%
Space Separator 51697
 
9.2%
Other Punctuation 4678
 
0.8%
Decimal Number 1180
 
0.2%
Dash Punctuation 682
 
0.1%
Close Punctuation 69
 
< 0.1%
Other Letter 67
 
< 0.1%
Open Punctuation 60
 
< 0.1%
Currency Symbol 51
 
< 0.1%
Other values (3) 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52071
12.8%
a 43216
10.6%
n 35841
 
8.8%
i 33715
 
8.3%
r 33334
 
8.2%
o 32189
 
7.9%
l 23372
 
5.8%
s 22072
 
5.4%
t 21981
 
5.4%
h 15972
 
3.9%
Other values (43) 92388
22.7%
Uppercase Letter
ValueCountFrequency (%)
T 8808
 
9.2%
B 8040
 
8.4%
S 7885
 
8.3%
M 6547
 
6.9%
C 6411
 
6.7%
J 5548
 
5.8%
D 5221
 
5.5%
A 4697
 
4.9%
R 4671
 
4.9%
F 4631
 
4.9%
Other values (25) 32813
34.4%
Other Letter
ValueCountFrequency (%)
7
 
10.4%
7
 
10.4%
6
 
9.0%
4
 
6.0%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (18) 30
44.8%
Other Punctuation
ValueCountFrequency (%)
& 1808
38.6%
. 1693
36.2%
' 548
 
11.7%
, 350
 
7.5%
" 111
 
2.4%
! 97
 
2.1%
/ 47
 
1.0%
: 9
 
0.2%
* 7
 
0.1%
? 5
 
0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 210
17.8%
0 176
14.9%
4 150
12.7%
5 138
11.7%
1 120
10.2%
9 103
8.7%
3 96
8.1%
6 77
 
6.5%
8 71
 
6.0%
7 39
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 65
94.2%
] 4
 
5.8%
Open Punctuation
ValueCountFrequency (%)
( 56
93.3%
[ 4
 
6.7%
Space Separator
ValueCountFrequency (%)
51697
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 682
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 51
100.0%
Math Symbol
ValueCountFrequency (%)
+ 27
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 501423
89.5%
Common 58447
 
10.4%
Katakana 50
 
< 0.1%
Han 17
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52071
 
10.4%
a 43216
 
8.6%
n 35841
 
7.1%
i 33715
 
6.7%
r 33334
 
6.6%
o 32189
 
6.4%
l 23372
 
4.7%
s 22072
 
4.4%
t 21981
 
4.4%
h 15972
 
3.2%
Other values (78) 187660
37.4%
Common
ValueCountFrequency (%)
51697
88.5%
& 1808
 
3.1%
. 1693
 
2.9%
- 682
 
1.2%
' 548
 
0.9%
, 350
 
0.6%
2 210
 
0.4%
0 176
 
0.3%
4 150
 
0.3%
5 138
 
0.2%
Other values (22) 995
 
1.7%
Katakana
ValueCountFrequency (%)
6
 
12.0%
4
 
8.0%
3
 
6.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
Other values (13) 23
46.0%
Han
ValueCountFrequency (%)
7
41.2%
7
41.2%
1
 
5.9%
1
 
5.9%
1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 558285
99.7%
None 1580
 
0.3%
Katakana 54
 
< 0.1%
CJK 17
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52071
 
9.3%
51697
 
9.3%
a 43216
 
7.7%
n 35841
 
6.4%
i 33715
 
6.0%
r 33334
 
6.0%
o 32189
 
5.8%
l 23372
 
4.2%
s 22072
 
4.0%
t 21981
 
3.9%
Other values (70) 208797
37.4%
None
ValueCountFrequency (%)
é 357
22.6%
í 210
13.3%
á 201
12.7%
ô 118
 
7.5%
ç 110
 
7.0%
ó 86
 
5.4%
ü 83
 
5.3%
ã 69
 
4.4%
É 49
 
3.1%
ö 47
 
3.0%
Other values (27) 250
15.8%
CJK
ValueCountFrequency (%)
7
41.2%
7
41.2%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Katakana
ValueCountFrequency (%)
6
 
11.1%
4
 
7.4%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (15) 27
50.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

uri
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct40560
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
spotify:track:2fQ6sBFWaLv2Gxos4igHLy
 
3
spotify:track:0wz1LjDb9ZNEYwOmDJ3Q4b
 
3
spotify:track:0hA8G8smCwi1h1nmxyRqT3
 
3
spotify:track:7vvRkLPIvfjjmCIqNxBuEZ
 
3
spotify:track:3EgvmOhP3NQUHY7d6PDOUg
 
3
Other values (40555)
41091 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1479816
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40023 ?
Unique (%)97.4%

Sample

1st rowspotify:track:16BjtTPPOnzv83TQWrnQQf
2nd rowspotify:track:3Uvx1TO0Kg5HgGPk58lHXv
3rd rowspotify:track:1J1Z0XIL18hClKmm5T5ytO
4th rowspotify:track:0mrP69xWBmlnixuVLFqCSl
5th rowspotify:track:15fxd1I7i8BFsZhKwoKANr

Common Values

ValueCountFrequency (%)
spotify:track:2fQ6sBFWaLv2Gxos4igHLy 3
 
< 0.1%
spotify:track:0wz1LjDb9ZNEYwOmDJ3Q4b 3
 
< 0.1%
spotify:track:0hA8G8smCwi1h1nmxyRqT3 3
 
< 0.1%
spotify:track:7vvRkLPIvfjjmCIqNxBuEZ 3
 
< 0.1%
spotify:track:3EgvmOhP3NQUHY7d6PDOUg 3
 
< 0.1%
spotify:track:3mRM4NM8iO7UBqrSigCQFH 3
 
< 0.1%
spotify:track:7tFiyTwD0nx5a1eklYtX2J 3
 
< 0.1%
spotify:track:0RgcOUQg4qYAEt9RIdf3oB 3
 
< 0.1%
spotify:track:4gGYiGsxhPYpsGIttWLwlT 3
 
< 0.1%
spotify:track:1E8wif6bVXurUgxV8Gfwrw 2
 
< 0.1%
Other values (40550) 41077
99.9%

Length

2023-09-26T15:11:47.950770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:2fq6sbfwalv2gxos4ighly 3
 
< 0.1%
spotify:track:0ha8g8smcwi1h1nmxyrqt3 3
 
< 0.1%
spotify:track:7vvrklpivfjjmciqnxbuez 3
 
< 0.1%
spotify:track:3egvmohp3nquhy7d6pdoug 3
 
< 0.1%
spotify:track:3mrm4nm8io7ubqrsigcqfh 3
 
< 0.1%
spotify:track:7tfiytwd0nx5a1eklytx2j 3
 
< 0.1%
spotify:track:0rgcouqg4qyaet9ridf3ob 3
 
< 0.1%
spotify:track:4ggyigsxhpypsgittwlwlt 3
 
< 0.1%
spotify:track:0wz1ljdb9zneywomdj3q4b 3
 
< 0.1%
spotify:track:1wttltofvcjqm3sxsmkddx 2
 
< 0.1%
Other values (40550) 41077
99.9%

Most occurring characters

ValueCountFrequency (%)
t 96179
 
6.5%
: 82212
 
5.6%
i 55181
 
3.7%
p 55174
 
3.7%
c 55127
 
3.7%
a 55083
 
3.7%
s 55073
 
3.7%
o 55068
 
3.7%
k 55056
 
3.7%
y 54986
 
3.7%
Other values (53) 860677
58.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 856395
57.9%
Uppercase Letter 360449
24.4%
Decimal Number 180760
 
12.2%
Other Punctuation 82212
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 96179
 
11.2%
i 55181
 
6.4%
p 55174
 
6.4%
c 55127
 
6.4%
a 55083
 
6.4%
s 55073
 
6.4%
o 55068
 
6.4%
k 55056
 
6.4%
y 54986
 
6.4%
r 54973
 
6.4%
Other values (16) 264495
30.9%
Uppercase Letter
ValueCountFrequency (%)
A 14155
 
3.9%
I 14117
 
3.9%
H 14066
 
3.9%
J 13985
 
3.9%
E 13970
 
3.9%
V 13963
 
3.9%
C 13956
 
3.9%
F 13953
 
3.9%
Q 13924
 
3.9%
Z 13907
 
3.9%
Other values (16) 220453
61.2%
Decimal Number
ValueCountFrequency (%)
1 19529
10.8%
5 19287
10.7%
6 19243
10.6%
4 19175
10.6%
0 19175
10.6%
3 19160
10.6%
2 19137
10.6%
7 18065
10.0%
9 14042
7.8%
8 13947
7.7%
Other Punctuation
ValueCountFrequency (%)
: 82212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1216844
82.2%
Common 262972
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 96179
 
7.9%
i 55181
 
4.5%
p 55174
 
4.5%
c 55127
 
4.5%
a 55083
 
4.5%
s 55073
 
4.5%
o 55068
 
4.5%
k 55056
 
4.5%
y 54986
 
4.5%
r 54973
 
4.5%
Other values (42) 624944
51.4%
Common
ValueCountFrequency (%)
: 82212
31.3%
1 19529
 
7.4%
5 19287
 
7.3%
6 19243
 
7.3%
4 19175
 
7.3%
0 19175
 
7.3%
3 19160
 
7.3%
2 19137
 
7.3%
7 18065
 
6.9%
9 14042
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1479816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 96179
 
6.5%
: 82212
 
5.6%
i 55181
 
3.7%
p 55174
 
3.7%
c 55127
 
3.7%
a 55083
 
3.7%
s 55073
 
3.7%
o 55068
 
3.7%
k 55056
 
3.7%
y 54986
 
3.7%
Other values (53) 860677
58.2%

danceability
Real number (ℝ)

Distinct1048
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53969496
Minimum0
Maximum0.988
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:48.240293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.219
Q10.42
median0.552
Q30.669
95-th percentile0.815
Maximum0.988
Range0.988
Interquartile range (IQR)0.249

Descriptive statistics

Standard deviation0.17782076
Coefficient of variation (CV)0.32948383
Kurtosis-0.42377912
Mean0.53969496
Median Absolute Deviation (MAD)0.123
Skewness-0.2517624
Sum22184.701
Variance0.031620224
MonotonicityNot monotonic
2023-09-26T15:11:48.554898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 116
 
0.3%
0.567 111
 
0.3%
0.652 108
 
0.3%
0.583 107
 
0.3%
0.551 106
 
0.3%
0.657 105
 
0.3%
0.61 102
 
0.2%
0.624 102
 
0.2%
0.6 101
 
0.2%
0.558 101
 
0.2%
Other values (1038) 40047
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.0576 1
< 0.1%
0.0588 1
< 0.1%
0.0593 1
< 0.1%
0.0596 2
< 0.1%
0.0597 1
< 0.1%
0.06 1
< 0.1%
0.0609 2
< 0.1%
0.061 1
< 0.1%
0.0617 1
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.986 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 2
< 0.1%
0.979 1
 
< 0.1%
0.978 3
< 0.1%
0.976 1
 
< 0.1%
0.974 3
< 0.1%
0.972 1
 
< 0.1%
0.97 1
 
< 0.1%

energy
Real number (ℝ)

Distinct1787
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57954489
Minimum0.000251
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:49.511739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.000251
5-th percentile0.12225
Q10.396
median0.601
Q30.787
95-th percentile0.948
Maximum1
Range0.999749
Interquartile range (IQR)0.391

Descriptive statistics

Standard deviation0.25262834
Coefficient of variation (CV)0.43590815
Kurtosis-0.79955249
Mean0.57954489
Median Absolute Deviation (MAD)0.195
Skewness-0.32016812
Sum23822.772
Variance0.063821079
MonotonicityNot monotonic
2023-09-26T15:11:50.058249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.641 80
 
0.2%
0.727 78
 
0.2%
0.721 74
 
0.2%
0.621 74
 
0.2%
0.723 73
 
0.2%
0.724 73
 
0.2%
0.683 71
 
0.2%
0.498 71
 
0.2%
0.72 71
 
0.2%
0.695 70
 
0.2%
Other values (1777) 40371
98.2%
ValueCountFrequency (%)
0.000251 1
< 0.1%
0.000276 1
< 0.1%
0.000348 1
< 0.1%
0.000357 1
< 0.1%
0.000419 1
< 0.1%
0.000576 1
< 0.1%
0.000628 1
< 0.1%
0.000707 1
< 0.1%
0.00093 1
< 0.1%
0.000982 1
< 0.1%
ValueCountFrequency (%)
1 4
 
< 0.1%
0.999 16
 
< 0.1%
0.998 24
0.1%
0.997 31
0.1%
0.996 30
0.1%
0.995 46
0.1%
0.994 30
0.1%
0.993 38
0.1%
0.992 32
0.1%
0.991 42
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2135941
Minimum0
Maximum11
Zeros5130
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:50.544161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.534977
Coefficient of variation (CV)0.67803072
Kurtosis-1.2773755
Mean5.2135941
Median Absolute Deviation (MAD)3
Skewness0.011140421
Sum214310
Variance12.496062
MonotonicityNot monotonic
2023-09-26T15:11:50.982996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5130
12.5%
7 4962
12.1%
2 4619
11.2%
9 4469
10.9%
5 3790
9.2%
4 3323
8.1%
1 3278
8.0%
11 2815
6.8%
10 2728
6.6%
8 2345
5.7%
Other values (2) 3647
8.9%
ValueCountFrequency (%)
0 5130
12.5%
1 3278
8.0%
2 4619
11.2%
3 1430
 
3.5%
4 3323
8.1%
5 3790
9.2%
6 2217
5.4%
7 4962
12.1%
8 2345
5.7%
9 4469
10.9%
ValueCountFrequency (%)
11 2815
6.8%
10 2728
6.6%
9 4469
10.9%
8 2345
5.7%
7 4962
12.1%
6 2217
5.4%
5 3790
9.2%
4 3323
8.1%
3 1430
 
3.5%
2 4619
11.2%

loudness
Real number (ℝ)

Distinct16160
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.221525
Minimum-49.253
Maximum3.744
Zeros0
Zeros (%)0.0%
Negative41100
Negative (%)> 99.9%
Memory size321.3 KiB
2023-09-26T15:11:51.497263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-49.253
5-th percentile-20.24
Q1-12.816
median-9.257
Q3-6.37425
95-th percentile-3.81725
Maximum3.744
Range52.997
Interquartile range (IQR)6.44175

Descriptive statistics

Standard deviation5.3116258
Coefficient of variation (CV)-0.519651
Kurtosis3.3100306
Mean-10.221525
Median Absolute Deviation (MAD)3.144
Skewness-1.4151087
Sum-420166.02
Variance28.213369
MonotonicityNot monotonic
2023-09-26T15:11:52.026617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.142 15
 
< 0.1%
-17.135 12
 
< 0.1%
-6.293 12
 
< 0.1%
-7.346 12
 
< 0.1%
-9.037 12
 
< 0.1%
-9.174 11
 
< 0.1%
-6.012 11
 
< 0.1%
-8.264 11
 
< 0.1%
-10.728 11
 
< 0.1%
-9.309 11
 
< 0.1%
Other values (16150) 40988
99.7%
ValueCountFrequency (%)
-49.253 1
< 0.1%
-47.327 1
< 0.1%
-46.655 1
< 0.1%
-44.347 1
< 0.1%
-43.989 1
< 0.1%
-43.178 1
< 0.1%
-43.06 1
< 0.1%
-42.959 1
< 0.1%
-42.66 1
< 0.1%
-41.643 1
< 0.1%
ValueCountFrequency (%)
3.744 1
< 0.1%
2.291 1
< 0.1%
1.963 1
< 0.1%
1.137 1
< 0.1%
0.878 1
< 0.1%
0.45 1
< 0.1%
-0.149 1
< 0.1%
-0.155 1
< 0.1%
-0.223 1
< 0.1%
-0.296 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
1
28501 
0
12605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

Length

2023-09-26T15:11:52.299847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-26T15:11:52.572968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

Most occurring characters

ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41106
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

Most occurring scripts

ValueCountFrequency (%)
Common 41106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28501
69.3%
0 12605
30.7%

speechiness
Real number (ℝ)

Distinct1346
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.072960458
Minimum0
Maximum0.96
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:52.827023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0277
Q10.0337
median0.0434
Q30.0698
95-th percentile0.239
Maximum0.96
Range0.96
Interquartile range (IQR)0.0361

Descriptive statistics

Standard deviation0.086111672
Coefficient of variation (CV)1.1802512
Kurtosis29.967468
Mean0.072960458
Median Absolute Deviation (MAD)0.0124
Skewness4.5733761
Sum2999.1126
Variance0.00741522
MonotonicityNot monotonic
2023-09-26T15:11:53.135149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0331 170
 
0.4%
0.0295 167
 
0.4%
0.0298 163
 
0.4%
0.0339 161
 
0.4%
0.033 158
 
0.4%
0.029 155
 
0.4%
0.0307 155
 
0.4%
0.0344 155
 
0.4%
0.0306 154
 
0.4%
0.0294 154
 
0.4%
Other values (1336) 39514
96.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.022 1
 
< 0.1%
0.0221 1
 
< 0.1%
0.0223 1
 
< 0.1%
0.0224 4
< 0.1%
0.0225 3
< 0.1%
0.0226 1
 
< 0.1%
0.0227 2
 
< 0.1%
0.0228 5
< 0.1%
0.0229 2
 
< 0.1%
ValueCountFrequency (%)
0.96 1
 
< 0.1%
0.957 1
 
< 0.1%
0.956 1
 
< 0.1%
0.955 1
 
< 0.1%
0.954 1
 
< 0.1%
0.952 2
< 0.1%
0.951 1
 
< 0.1%
0.95 3
< 0.1%
0.949 1
 
< 0.1%
0.948 2
< 0.1%

acousticness
Real number (ℝ)

Distinct4194
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36419744
Minimum0
Maximum0.996
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:53.448777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000309
Q10.0394
median0.258
Q30.676
95-th percentile0.959
Maximum0.996
Range0.996
Interquartile range (IQR)0.6366

Descriptive statistics

Standard deviation0.33891296
Coefficient of variation (CV)0.93057479
Kurtosis-1.2425007
Mean0.36419744
Median Absolute Deviation (MAD)0.24847
Skewness0.49336049
Sum14970.7
Variance0.11486199
MonotonicityNot monotonic
2023-09-26T15:11:53.747484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 112
 
0.3%
0.994 95
 
0.2%
0.992 86
 
0.2%
0.993 86
 
0.2%
0.99 80
 
0.2%
0.991 72
 
0.2%
0.989 70
 
0.2%
0.98 67
 
0.2%
0.984 66
 
0.2%
0.102 66
 
0.2%
Other values (4184) 40306
98.1%
ValueCountFrequency (%)
0 8
< 0.1%
1.01 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-61
 
< 0.1%
1.05 × 10-61
 
< 0.1%
1.06 × 10-61
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.11 × 10-62
 
< 0.1%
1.12 × 10-61
 
< 0.1%
1.13 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 32
 
0.1%
0.995 112
0.3%
0.994 95
0.2%
0.993 86
0.2%
0.992 86
0.2%
0.991 72
0.2%
0.99 80
0.2%
0.989 70
0.2%
0.988 53
0.1%
0.987 54
0.1%

instrumentalness
Real number (ℝ)

Distinct5122
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1544161
Minimum0
Maximum1
Zeros11709
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:54.041623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00012
Q30.06125
95-th percentile0.898
Maximum1
Range1
Interquartile range (IQR)0.06125

Descriptive statistics

Standard deviation0.30353008
Coefficient of variation (CV)1.9656634
Kurtosis1.3480587
Mean0.1544161
Median Absolute Deviation (MAD)0.00012
Skewness1.7452773
Sum6347.4283
Variance0.09213051
MonotonicityNot monotonic
2023-09-26T15:11:54.370697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11709
28.5%
0.893 43
 
0.1%
0.907 43
 
0.1%
0.902 42
 
0.1%
0.915 40
 
0.1%
0.908 40
 
0.1%
0.903 39
 
0.1%
0.896 39
 
0.1%
0.926 38
 
0.1%
0.91 38
 
0.1%
Other values (5112) 29035
70.6%
ValueCountFrequency (%)
0 11709
28.5%
1 × 10-67
 
< 0.1%
1.01 × 10-624
 
0.1%
1.02 × 10-615
 
< 0.1%
1.03 × 10-619
 
< 0.1%
1.04 × 10-69
 
< 0.1%
1.05 × 10-613
 
< 0.1%
1.06 × 10-614
 
< 0.1%
1.07 × 10-613
 
< 0.1%
1.08 × 10-615
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.999 4
< 0.1%
0.998 3
< 0.1%
0.997 1
 
< 0.1%
0.995 2
 
< 0.1%
0.994 1
 
< 0.1%
0.993 6
< 0.1%
0.992 2
 
< 0.1%
0.991 3
< 0.1%
0.99 4
< 0.1%

liveness
Real number (ℝ)

Distinct1674
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20153462
Minimum0.013
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:54.686999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.013
5-th percentile0.054725
Q10.094
median0.132
Q30.261
95-th percentile0.597
Maximum0.999
Range0.986
Interquartile range (IQR)0.167

Descriptive statistics

Standard deviation0.17295927
Coefficient of variation (CV)0.85821121
Kurtosis4.925329
Mean0.20153462
Median Absolute Deviation (MAD)0.0549
Skewness2.1238183
Sum8284.2822
Variance0.02991491
MonotonicityNot monotonic
2023-09-26T15:11:54.989556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 395
 
1.0%
0.11 373
 
0.9%
0.107 363
 
0.9%
0.106 349
 
0.8%
0.104 346
 
0.8%
0.108 343
 
0.8%
0.109 341
 
0.8%
0.105 335
 
0.8%
0.114 331
 
0.8%
0.112 330
 
0.8%
Other values (1664) 37600
91.5%
ValueCountFrequency (%)
0.013 1
< 0.1%
0.0136 1
< 0.1%
0.0146 2
< 0.1%
0.015 1
< 0.1%
0.0166 1
< 0.1%
0.0167 1
< 0.1%
0.0169 1
< 0.1%
0.0184 1
< 0.1%
0.0186 1
< 0.1%
0.0189 1
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.997 1
 
< 0.1%
0.993 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 2
< 0.1%
0.989 4
< 0.1%
0.988 2
< 0.1%
0.987 3
< 0.1%
0.986 1
 
< 0.1%
0.985 3
< 0.1%

valence
Real number (ℝ)

Distinct1609
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54244037
Minimum0
Maximum0.996
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:55.296604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08015
Q10.33
median0.558
Q30.768
95-th percentile0.945
Maximum0.996
Range0.996
Interquartile range (IQR)0.438

Descriptive statistics

Standard deviation0.26732894
Coefficient of variation (CV)0.49282641
Kurtosis-1.051565
Mean0.54244037
Median Absolute Deviation (MAD)0.218
Skewness-0.17974501
Sum22297.554
Variance0.071464761
MonotonicityNot monotonic
2023-09-26T15:11:55.630081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 217
 
0.5%
0.962 167
 
0.4%
0.963 165
 
0.4%
0.964 145
 
0.4%
0.96 137
 
0.3%
0.965 117
 
0.3%
0.966 90
 
0.2%
0.967 82
 
0.2%
0.763 75
 
0.2%
0.594 72
 
0.2%
Other values (1599) 39839
96.9%
ValueCountFrequency (%)
0 14
< 0.1%
1 × 10-510
< 0.1%
0.00336 1
 
< 0.1%
0.00364 1
 
< 0.1%
0.00457 1
 
< 0.1%
0.00516 1
 
< 0.1%
0.00953 1
 
< 0.1%
0.0113 1
 
< 0.1%
0.013 1
 
< 0.1%
0.0144 1
 
< 0.1%
ValueCountFrequency (%)
0.996 1
 
< 0.1%
0.993 1
 
< 0.1%
0.991 2
 
< 0.1%
0.99 2
 
< 0.1%
0.988 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 4
< 0.1%
0.983 6
< 0.1%
0.982 4
< 0.1%

tempo
Real number (ℝ)

Distinct32152
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.33825
Minimum0
Maximum241.423
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:55.927523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.391
Q197.397
median117.565
Q3136.494
95-th percentile174.359
Maximum241.423
Range241.423
Interquartile range (IQR)39.097

Descriptive statistics

Standard deviation29.098845
Coefficient of variation (CV)0.24383503
Kurtosis-0.059152308
Mean119.33825
Median Absolute Deviation (MAD)19.6275
Skewness0.48527837
Sum4905518.1
Variance846.7428
MonotonicityNot monotonic
2023-09-26T15:11:56.227891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.993 15
 
< 0.1%
142.187 12
 
< 0.1%
119.987 12
 
< 0.1%
119.999 10
 
< 0.1%
119.961 9
 
< 0.1%
129.988 9
 
< 0.1%
94.997 9
 
< 0.1%
100.005 8
 
< 0.1%
100.017 8
 
< 0.1%
120.012 8
 
< 0.1%
Other values (32142) 41006
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
31.988 1
< 0.1%
32.435 1
< 0.1%
34.333 1
< 0.1%
34.496 1
< 0.1%
34.535 1
< 0.1%
35.732 1
< 0.1%
36.52 1
< 0.1%
37.114 1
< 0.1%
39.002 1
< 0.1%
ValueCountFrequency (%)
241.423 1
< 0.1%
241.009 1
< 0.1%
233.429 1
< 0.1%
217.943 1
< 0.1%
217.872 1
< 0.1%
217.396 1
< 0.1%
214.848 2
< 0.1%
214.121 1
< 0.1%
213.233 1
< 0.1%
212.9 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct21517
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234877.55
Minimum15168
Maximum4170227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:56.549310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15168
5-th percentile120673.5
Q1172927.75
median217907
Q3266773
95-th percentile402224
Maximum4170227
Range4155059
Interquartile range (IQR)93845.25

Descriptive statistics

Standard deviation118967.4
Coefficient of variation (CV)0.50650817
Kurtosis122.19412
Mean234877.55
Median Absolute Deviation (MAD)46720
Skewness6.8206745
Sum9.6548766 × 109
Variance1.4153242 × 1010
MonotonicityNot monotonic
2023-09-26T15:11:56.869897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321853 12
 
< 0.1%
192000 12
 
< 0.1%
196000 12
 
< 0.1%
240000 12
 
< 0.1%
228867 11
 
< 0.1%
208000 11
 
< 0.1%
180000 11
 
< 0.1%
238267 11
 
< 0.1%
209000 11
 
< 0.1%
176000 11
 
< 0.1%
Other values (21507) 40992
99.7%
ValueCountFrequency (%)
15168 1
< 0.1%
15629 1
< 0.1%
15920 1
< 0.1%
19533 1
< 0.1%
20493 1
< 0.1%
20573 1
< 0.1%
21587 1
< 0.1%
21950 1
< 0.1%
22215 1
< 0.1%
22957 1
< 0.1%
ValueCountFrequency (%)
4170227 1
< 0.1%
3816373 1
< 0.1%
3791480 1
< 0.1%
3391040 1
< 0.1%
2685093 1
< 0.1%
2623952 1
< 0.1%
2516987 1
< 0.1%
2223827 1
< 0.1%
2159427 1
< 0.1%
2104347 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
4
36297 
3
3840 
5
 
595
1
 
371
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41106
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

Length

2023-09-26T15:11:57.166804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-26T15:11:57.466862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41106
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 41106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 36297
88.3%
3 3840
 
9.3%
5 595
 
1.4%
1 371
 
0.9%
0 3
 
< 0.1%

chorus_hit
Real number (ℝ)

Distinct39950
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.106041
Minimum0
Maximum433.182
Zeros157
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:57.747035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.663528
Q127.599792
median35.850795
Q347.625615
95-th percentile75.633672
Maximum433.182
Range433.182
Interquartile range (IQR)20.025822

Descriptive statistics

Standard deviation19.005515
Coefficient of variation (CV)0.4738816
Kurtosis12.435734
Mean40.106041
Median Absolute Deviation (MAD)9.525285
Skewness2.2153385
Sum1648598.9
Variance361.2096
MonotonicityNot monotonic
2023-09-26T15:11:58.047163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.4%
60.94077 12
 
< 0.1%
58.49512 8
 
< 0.1%
97.5751 6
 
< 0.1%
26.76235 5
 
< 0.1%
30.50348 5
 
< 0.1%
19.88463 5
 
< 0.1%
24.04446 3
 
< 0.1%
31.43593 3
 
< 0.1%
55.32422 3
 
< 0.1%
Other values (39940) 40899
99.5%
ValueCountFrequency (%)
0 157
0.4%
4.14117 1
 
< 0.1%
4.1643 1
 
< 0.1%
4.51267 1
 
< 0.1%
4.98552 1
 
< 0.1%
5.28582 1
 
< 0.1%
5.33464 1
 
< 0.1%
5.37369 1
 
< 0.1%
5.38553 1
 
< 0.1%
5.51701 1
 
< 0.1%
ValueCountFrequency (%)
433.182 1
< 0.1%
262.6154 1
< 0.1%
235.61008 1
< 0.1%
235.06074 1
< 0.1%
224.07663 1
< 0.1%
220.48609 1
< 0.1%
220.0245 1
< 0.1%
219.63624 1
< 0.1%
213.15499 1
< 0.1%
208.48624 1
< 0.1%

sections
Real number (ℝ)

Distinct84
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.475673
Minimum0
Maximum169
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:58.345587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q312
95-th percentile17
Maximum169
Range169
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8718505
Coefficient of variation (CV)0.46506326
Kurtosis105.87548
Mean10.475673
Median Absolute Deviation (MAD)2
Skewness6.0535885
Sum430613
Variance23.734927
MonotonicityNot monotonic
2023-09-26T15:11:58.666859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 5583
13.6%
10 5275
12.8%
8 4888
11.9%
11 4521
11.0%
7 3710
9.0%
12 3609
8.8%
13 2536
6.2%
6 2327
5.7%
14 1753
 
4.3%
5 1260
 
3.1%
Other values (74) 5644
13.7%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 11
 
< 0.1%
2 131
 
0.3%
3 333
 
0.8%
4 660
 
1.6%
5 1260
 
3.1%
6 2327
5.7%
7 3710
9.0%
8 4888
11.9%
9 5583
13.6%
ValueCountFrequency (%)
169 1
< 0.1%
159 1
< 0.1%
145 1
< 0.1%
130 1
< 0.1%
109 1
< 0.1%
101 1
< 0.1%
97 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
82 1
< 0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.3 KiB
0
20553 
1
20553 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

Length

2023-09-26T15:11:58.963790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-26T15:11:59.216710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

Most occurring characters

ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41106
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20553
50.0%
1 20553
50.0%

decade
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1982.7753
Minimum1960
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.3 KiB
2023-09-26T15:11:59.403396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1960
Q11970
median1980
Q32000
95-th percentile2010
Maximum2010
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.491234
Coefficient of variation (CV)0.0088215918
Kurtosis-1.2899609
Mean1982.7753
Median Absolute Deviation (MAD)20
Skewness0.19439326
Sum81503960
Variance305.94326
MonotonicityNot monotonic
2023-09-26T15:11:59.656685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1960 8642
21.0%
1970 7766
18.9%
1980 6908
16.8%
2010 6398
15.6%
2000 5872
14.3%
1990 5520
13.4%
ValueCountFrequency (%)
1960 8642
21.0%
1970 7766
18.9%
1980 6908
16.8%
1990 5520
13.4%
2000 5872
14.3%
2010 6398
15.6%
ValueCountFrequency (%)
2010 6398
15.6%
2000 5872
14.3%
1990 5520
13.4%
1980 6908
16.8%
1970 7766
18.9%
1960 8642
21.0%

Interactions

2023-09-26T15:11:42.035147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:34.647911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:43.646507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:50.656320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:59.477809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:05.011401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:09.074902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:13.512432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:17.539089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:21.065807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:25.959240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:29.573751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:33.505320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:38.170937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:42.290425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:35.424303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:44.077088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:51.107933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:00.226017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:05.278858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:09.442986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:13.781709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:17.783595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:21.322434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:26.209738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:29.828089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:33.767459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:38.606230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:42.543098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:35.956487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:44.513710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:51.468257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:00.753847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:05.535553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:09.848248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:14.049192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:18.027647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:21.640400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:26.472522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:30.073000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:34.023003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:38.912570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:42.818683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:36.509606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:45.013092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:51.905783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:01.070111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:05.811342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:10.252124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:14.305957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:18.271820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:22.043143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:26.738026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:30.322877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:34.273585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:39.168273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:43.078590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:37.053858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:45.435739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:52.406065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:01.448998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:06.073732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:10.630872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:14.551948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:18.540494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:22.410398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:26.987689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:30.592949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:34.523236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:39.425299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:43.343993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:37.462250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:45.899384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:53.150296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:01.836019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:06.317625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:11.071127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:14.822782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:18.799909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:22.815019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:27.236858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:30.841681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:34.798156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:39.694010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:43.592612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:37.922096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:46.356849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:53.749701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:02.284334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:06.578974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:11.466319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:15.096152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:19.046841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:23.228134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:27.503770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:31.482518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:35.099287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:39.961611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:43.854234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:38.605864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:47.156952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:54.721557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:02.708116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:06.844318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:11.728801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:15.344918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:19.322500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:23.634054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:27.757859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:31.746585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:35.431998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:40.213865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:44.107864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:39.227948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:47.645653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:55.551591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:03.192530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:07.091083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:11.980586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:15.603224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:19.564080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:24.048397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:28.003671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:31.992773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:35.851406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:40.481109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:44.387897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:39.756535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:48.118783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:56.294055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:03.481649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:07.338977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:12.224250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:15.858446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:19.811816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:24.463573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:28.251182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:32.248892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:36.248816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:40.738191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:44.635028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:40.625615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:48.670470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:57.083311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:03.737899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:07.591060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:12.475127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:16.116246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:20.050673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:24.894845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:28.528481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:32.490237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:36.599660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:41.001382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:44.900311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:41.476677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:49.150073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:57.883727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:03.985646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:07.872474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:12.727597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:16.725941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:20.309247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:25.175250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:28.790765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:32.742907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:37.015335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:41.252564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:45.163812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:42.088431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:49.646271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:58.217417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:04.472842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:08.259742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:12.993758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:16.995821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:20.547732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:25.424131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:29.029549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:32.984380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:37.364796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:41.502379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:45.433864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:42.968851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:50.146959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:10:58.701485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:04.757070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:08.665865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:13.248619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:17.277741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:20.815881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:25.701719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:29.297765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:33.257409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:37.758233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-26T15:11:41.768211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-26T15:11:59.893970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
danceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mschorus_hitsectionsdecademodetime_signaturetarget
danceability1.0000.1640.0150.1930.126-0.177-0.259-0.1510.534-0.0450.039-0.0230.0310.1440.0920.1490.346
energy0.1641.0000.0220.7560.314-0.704-0.1120.1250.3020.2250.117-0.0010.0350.3440.0490.1600.341
key0.0150.0221.0000.0110.034-0.026-0.004-0.0030.0050.0020.0190.0080.0060.0190.2350.0190.053
loudness0.1930.7560.0111.0000.175-0.546-0.3270.0820.1730.1520.058-0.0110.0000.4230.0370.1250.297
speechiness0.1260.3140.0340.1751.000-0.207-0.0240.0870.0410.0980.0360.008-0.0070.2160.0580.0590.081
acousticness-0.177-0.704-0.026-0.546-0.2071.0000.060-0.017-0.115-0.191-0.225-0.009-0.119-0.4350.0690.1470.325
instrumentalness-0.259-0.112-0.004-0.327-0.0240.0601.000-0.052-0.229-0.0330.1230.0440.082-0.0430.0770.0640.417
liveness-0.1510.125-0.0030.0820.087-0.017-0.0521.000-0.0300.020-0.0680.018-0.065-0.0400.0170.0080.053
valence0.5340.3020.0050.1730.041-0.115-0.229-0.0301.0000.110-0.171-0.044-0.103-0.1850.0480.1250.281
tempo-0.0450.2250.0020.1520.098-0.191-0.0330.0200.1101.0000.006-0.0470.0740.0760.0290.2970.100
duration_ms0.0390.1170.0190.0580.036-0.2250.123-0.068-0.1710.0061.0000.0750.7850.3150.0540.0210.142
chorus_hit-0.023-0.0010.008-0.0110.008-0.0090.0440.018-0.044-0.0470.0751.000-0.1610.0260.0210.0280.056
sections0.0310.0350.0060.000-0.007-0.1190.082-0.065-0.1030.0740.785-0.1611.0000.1840.0490.0230.152
decade0.1440.3440.0190.4230.216-0.435-0.043-0.040-0.1850.0760.3150.0260.1841.0000.0900.0680.000
mode0.0920.0490.2350.0370.0580.0690.0770.0170.0480.0290.0540.0210.0490.0901.0000.0130.079
time_signature0.1490.1600.0190.1250.0590.1470.0640.0080.1250.2970.0210.0280.0230.0680.0131.0000.155
target0.3460.3410.0530.2970.0810.3250.4170.0530.2810.1000.1420.0560.1520.0000.0790.1551.000

Missing values

2023-09-26T15:11:45.882016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-26T15:11:46.651545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstargetdecade
0Nachtlager N. Granada - The Lower Castle YardTraditionalspotify:track:16BjtTPPOnzv83TQWrnQQf0.5300.59210-16.46110.03660.9420000.9330000.39300.9160148.09883400421.26378601970
1How's It Going To BeThird Eye Blindspotify:track:3Uvx1TO0Kg5HgGPk58lHXv0.5620.5935-9.36210.02630.0032700.0013900.09670.574080.289253413422.58257811990
2Heavenly PerverseDimmu Borgirspotify:track:1J1Z0XIL18hClKmm5T5ytO0.1380.9814-3.79710.11600.0000160.8140000.07030.039997.579392813319.713081502000
3Era um Garoto Que Como Eu Amava os Beatles e os Rolling Stones (C'era un ragazzo)Os Incríveisspotify:track:0mrP69xWBmlnixuVLFqCSl0.5360.7134-9.20310.06790.2510000.0000000.94800.7720128.594209947466.80246801970
4Clavel SevillanoJavier Solísspotify:track:15fxd1I7i8BFsZhKwoKANr0.2960.4620-9.52110.03620.7820000.0000000.63200.4430141.942226000336.35407801960
5AmenHarry Belafontespotify:track:3Whq6dcW9vnRVEGI1hgzEr0.3690.3437-10.84410.04250.3800000.0000000.44900.388095.744184080356.88143701960
6Make You Rock (Bonus Track) - 4.0 VersionMonica Naranjospotify:track:3fcPLa6nqrA6yBg6FJXtck0.5630.7975-6.29100.06620.0020400.0000080.13000.2640123.035225493450.96562801990
7Throw That MetalI Heart Hiroshimaspotify:track:3tRtvuCmTOdx2dnqZjl6kH0.5660.6642-4.30310.03690.1560000.0000330.32500.9440148.606171520155.70141802000
8Get Up And Boogie (That's Right)Silver Conventionspotify:track:6kkHnD5hJK75kHRRR2zUJG0.8010.5457-14.87610.03060.1190000.0019400.07900.9660104.989170067434.29962811970
9La EntregaJavier Solísspotify:track:6d0wPn4Wwvy0a4tClmA6El0.2050.3652-13.19710.03790.6320000.0000000.15200.5890178.098169800442.85187801960
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstargetdecade
41096E Lucevan Le Stelle - InstrumentalMělník Philharmonic Orchestraspotify:track:2QDp4b9Gjk9FxpklHEoVIy0.09340.18810-16.34000.03990.9400000.9030000.11600.070385.711142339328.01194802010
41097We Gotta FightSham 69spotify:track:6xJ4fg5YO673fmqJ05Jtpa0.20200.90611-6.31900.10700.0002160.3140000.86100.5550181.870108200453.59378601970
41098Chipsy PianoClaude Larsonspotify:track:6c3vym97dJnkUhclAjmDI80.70900.8835-13.53310.06550.8170000.7460000.09070.8440105.9613410740.00000201980
41099Natural's Not In ItGang Of Fourspotify:track:7zGcnkHnMpileiI0H0aIgc0.76300.8400-6.97410.28800.0401000.0652000.01990.8950161.340186760424.88276802010
41100Gone Too FarEddie Rabbittspotify:track:648Ur5jhWSW43QuI2qXeII0.78700.6257-13.29710.03210.4220000.0000000.19400.9380131.620203773445.84043911980
41101Always TogetherAl Martinospotify:track:146vkvsbGH7xQYRKjAYEhG0.28100.2728-12.98210.03100.7350000.0024500.24100.3580105.912158000419.57412811960
41102Walk Away From LoveDavid Ruffinspotify:track:5cAGsX0EzCqKuQr7nui6T70.62000.80410-7.60610.09370.2480000.0000110.21300.7760102.265328920427.882811611970
41103Doing The FunkC. Da Afrospotify:track:0XIkVkScOkBHUH139SjaWz0.78300.8130-4.53810.04450.0208000.7750000.10300.6660101.995381176498.842561002010
41104Long Live LoveSandie Shawspotify:track:7007XzDvqzxH3pl84LkS9z0.53400.4680-8.76010.05710.5990000.0000000.23700.8980151.580162133435.692431011960
41105Rocket ManElton Johnspotify:track:3gdewACMIVMEWVbyb8O9sY0.60100.53210-9.11910.02860.4320000.0000060.09250.3410136.571281613456.671091111970